CN115577334B - Air traffic control safety data operation guarantee system and method based on multi-model fusion - Google Patents
Air traffic control safety data operation guarantee system and method based on multi-model fusion Download PDFInfo
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Abstract
The invention discloses an air traffic control safety data operation guaranteeing system and method based on multi-model fusion, wherein the system comprises an air traffic control joint department coordination module, an air traffic control data format unification module, an air traffic control safety data checking and detecting module, an air traffic control safety standard limiting module, an air traffic control requirement recognizing module, an air traffic control management updating module, an air traffic control participator calling management module, an air traffic control data supervision module, an air traffic control signal pushing module and an air traffic control remote sensing information feedback module.
Description
Technical Field
The invention relates to the field of air traffic control, in particular to an air traffic control safety data operation guaranteeing system and method based on multi-model fusion.
Background
With the continuous development of civil aviation technology, civil aviation has been applied to various aspects in life, and the civil aviation can be better utilized to perform high-altitude operation, so that manpower is saved, the working efficiency is high, the operation risk of the civil aviation airspace is mainly concentrated on collision risks caused by other manned aircrafts in the airspace, and even if relevant regulations and regulations of the operation of the civil aviation airspace are being continuously perfected, reports about dangerous approaching between the civil aviation and the manned aircrafts are also being continuously increased.
However, the air traffic control system of the civil aviation is not perfect, the mastering of the air flight environment of the civil aviation is still not comprehensive, most data are obtained through prediction and past flight experience, the environment of the civil aviation cannot be monitored in real time, the number of unexpected situations in the civil aviation is large, and the safety cannot be guaranteed.
Disclosure of Invention
In order to overcome the defects and shortcomings in the prior art, the invention provides an air traffic control safety data operation guaranteeing system and method based on multi-model fusion.
The technical scheme adopted by the invention is that the air traffic control safety data operation guarantee system based on multi-model fusion comprises: an air traffic control united department coordination module, an air traffic control data format unification module, an air traffic control safety data check and detection module, an air traffic control safety standard limiting module, an air traffic control requirement identification module, an air traffic control management updating module, an air traffic control participator calling management module, an air traffic control data supervision module, an air traffic control signal pushing module and an air traffic control remote sensing information feedback module, wherein,
the air traffic control joint department coordination module is used for carrying out air management safety data real-time update on the monitored air traffic control joint departments and establishing event processing flow among the joint departments, and is electrically connected with the air traffic control data format unification module;
The air traffic control data format unification module is used for updating air traffic control department air traffic control safety data of the air traffic control department coordination module in real time, supervising the air traffic control department air traffic control safety data to be controlled from an air traffic control department air traffic control safety database, preprocessing the air traffic control department air traffic control safety data to form a unified air traffic control safety data calling and archiving format, and electrically connecting the air traffic control data format unification module with the air traffic control department coordination module, the air traffic control safety data checking module and the air traffic control data supervising module respectively;
the air traffic control security data checking and detecting module is used for automatically finding out the abnormal air traffic control department air traffic control security data in the air traffic control department air traffic control security data table to be controlled after the standardization in the air traffic control data format unification module, the empty pipe safety data checking and detecting module is respectively and electrically connected with the air traffic control data format unifying module, the air traffic control joint department empty pipe safety data coordination empty pipe system generating and updating module and the air traffic control management updating module;
The air traffic control safety standard limiting module is used for carrying out consistency setting of safety standard coefficients and parameters on air traffic control safety data in the system, and the air traffic control combined department air traffic control safety data rule configuration module is electrically connected with the air traffic control requirement identification module;
the air traffic control requirement identification module is used for controlling self-identification or default coordination of air traffic control system coefficients and parameters according to the air traffic control system configuration module, the air traffic control data format unification module standardized existing air traffic control department air traffic control system security data to air traffic control system, and the air traffic control security data check detection module automatically discovers (digs) air traffic control department air traffic control security data to perform check and detection to perform coordination air traffic control system unification, and the air traffic control requirement identification module is respectively electrically connected with the air traffic control system configuration module, the air traffic control data format unification module and the air traffic control security data check detection module;
the air traffic control management updating module is used for supporting artificial intelligence to adjust and update the model coefficient and the parameter which are self-generated by the air traffic control requirement identification module, perfecting an air management system, or automatically identifying a new air management system by the artificial intelligence and sending the new air management system to a manager for confirmation, and is respectively and electrically connected with the air traffic control safety data check module and the air traffic control requirement identification module;
The air traffic control participant personnel call management module is used for completing unified coordination and management of all functional module staff in the system and distributing work tasks, and is electrically connected with the air traffic control system configuration module and the air traffic control signal pushing module respectively;
the air traffic control data supervision module is used for integrating the artificial intelligence self-identification rule of the air traffic control management updating module according to the air traffic control system of the air traffic control united department generated by the air traffic control requirement identification module, calculating the air traffic control united department air management safety data to be checked and detected newly, judging whether the air traffic control united department air management safety data of the newly added air traffic control united department has coordination or abnormal data, and the air traffic control data supervision module is electrically connected with the air traffic control requirement identification module, the air traffic control management updating module and the air traffic control signal pushing module respectively;
the air traffic control signal pushing module sends pushing of different service content signals to an air traffic control security data manager of an air traffic control joint department according to the calculation result of the air traffic control data supervision module, and the air traffic control signal pushing module is electrically connected with the air traffic control data supervision module and the air traffic control remote sensing information feedback module respectively;
The air traffic control remote sensing information feedback module is used for receiving remote sensing information feedback of air traffic control joint department air traffic control safety data manager on using service air traffic control safety data, and feeding back the air traffic control safety data remote sensing information to the air traffic control demand identification module for adjusting and optimizing the model according to artificial intelligent remote sensing information feedback, and the air traffic control remote sensing information feedback module is electrically connected with the air traffic control demand identification module and the air traffic control signal pushing module respectively.
The air traffic control data format unification module comprises an air traffic control department air management security data supervision module to be controlled and an air traffic control department air management security data calling and archiving format standardization module, wherein the air traffic control department air management security data supervision module to be controlled is used for carrying out supervision on air traffic control department air management security data to be controlled or existing normal air traffic control department air management security data of the air traffic control department air management security data to be controlled according to an air traffic control department air management security data supervision mode set by the air traffic control department coordination module consistency, and the air traffic control department air management security data calling and archiving format standardization module to be controlled is used for preprocessing the air traffic control department air management security data supervised by the air traffic control department air management security data supervision module to be controlled into the air traffic control requirement identification module and the standard air traffic control department air traffic control security data calling and archiving format identifiable by the air traffic control data supervision module.
Further, the empty pipe safety data checking and detecting module comprises an artificial intelligent air traffic control combined department empty pipe safety data screening sub-module and a staff air traffic control combined department empty pipe safety data pair screening sub-module, wherein the artificial intelligent air traffic control combined department empty pipe safety data screening sub-module is used for screening out the data which are automatically set and managed by the artificial intelligence, and the staff air traffic control combined department empty pipe safety data pair screening sub-module is used for screening out the data which are manually set and managed by the staff.
Further, the empty pipe system model of the empty pipe system configuration module is a monitoring model with dynamically updated real-time coefficients and parameters.
A method for guaranteeing the operation of air traffic control safety data based on multi-model fusion comprises the following steps:
a1, updating data in real time of air traffic control security data of an air traffic control joint department to be controlled;
step A2, according to the real-time updating of the data, the air traffic control department air traffic control security data to be controlled is supervised from an air traffic control department air traffic control security database, and the air traffic control department air traffic control security data preprocessing convention air traffic control security data is called and archived;
Step A3, searching and finding abnormal air traffic control department air traffic control safety data in the standardized air traffic control department air traffic control safety data table to conduct checking and detection;
step A4, carrying out consistency setting of safety standard coefficients and parameters on safety data of the hollow pipe in the system;
step A5, carrying out unified identification of a coordinated air traffic control system according to the air traffic control department air traffic control safety data standardized in the step A2, the air traffic control department air traffic control safety data subjected to investigation and detection in the step A3 and the super coefficient and the parameters of the automatic air traffic control system model in the step A4;
step A6, the artificial intelligence adjusts and updates the self-generated coordination empty pipe system in the step A5, and perfects the empty pipe system;
step A7, carrying out coordination calculation on air traffic control department air traffic control security data to be controlled according to the self-generated coordination empty pipe system in the step A5 and the coordination empty pipe system modified by the artificial intelligence in the step A6, and judging whether the newly added air traffic control department air traffic control security data has coordination or abnormal data;
and step A8, sending out the pushing of different service content signals to an air traffic control department air traffic control safety data manager according to the calculation result in the step A7.
Further, the step A8 further includes a step A9: and A5, receiving remote sensing information feedback of air traffic control united department air traffic control safety data manager to service air traffic control safety data, feeding the remote sensing information of the air traffic control safety data back to the step A5, and adjusting and optimizing the check and detection unified identification rule.
Further, the step A2 specifically includes:
step A21, updating in real time according to the data, and supervising the air traffic control department air traffic control safety data to be controlled or the existing normal air traffic control department air traffic control safety data of the air traffic control department air traffic control safety data to be controlled in an air traffic control department air traffic control safety database;
and step A22, calling and archiving the air traffic control department air traffic control safety data pre-processing agreed air traffic control safety data.
Further, the step A3 specifically includes:
step A31, screening air traffic control department air traffic control security data from an air traffic control department air traffic control security data table to be controlled;
step A32, automatically setting and managing air traffic control department air traffic control safety data according to the screened artificial intelligence, and calculating the fluctuation range of the air traffic control department air traffic control safety data of related service air traffic control departments to obtain abnormal frequency among the air traffic control department air traffic control safety data of the service air traffic control departments;
And step A33, screening out the data manually set and managed by the staff according to the obtained air traffic control safety data correlation degree of different air traffic control united departments.
Further, the step A5 specifically includes:
step A51, judging whether air traffic control department air traffic control security data to be controlled is new air traffic control department air traffic control security data to be controlled;
step A52, if the air traffic control department air traffic control system determines that the air traffic control department air traffic control system safety data is new, carrying out coordinated air traffic control system model coefficient and parameter learning;
step A53, if it is determined that the air traffic control combination department air traffic control security data is not new air traffic control combination department air traffic control security data, it is determined whether the current time point is an update time point of air traffic control combination department air traffic control security data to be checked. And if the updated time point is reached, updating and adjusting the coefficient and the parameter of the empty pipe system model to be coordinated.
Further, the step a52 specifically includes:
step A521, assume that two air traffic control joint departments air traffic control safety data variables corresponding to the checking data to be coordinated;
step A522, performing convolution calculation and feature recognition extraction according to the air traffic control security data of the existing air traffic control joint department to obtain a convolution calculation and feature recognition extraction model;
Step A523, calculating errors of the convolution calculation and the air traffic control safety data value of the feature recognition extraction model and the real air traffic control joint department;
and step A524, calculating the mean and covariance of the existing errors to obtain coefficients and parameters of the convolutional neural network algorithm.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the air traffic control joint department air traffic control safety data operation guarantee system and method based on multi-model fusion can automatically find the air traffic control joint department air traffic control safety data for checking and detecting, and greatly improve the efficiency, accuracy and coverage rate of the air traffic control joint department air traffic control safety data checking and detecting;
2. after finding out the data for checking and detecting, the air traffic control department air traffic control security data coordination air traffic control system is automatically learned according to the existing value of the air traffic control department air traffic control security data, so that the management, efficiency and coverage of the air traffic control department air traffic control security data checking and detecting are improved, and meanwhile the efficiency of air traffic control is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention. In the drawings:
FIG. 1 is a schematic diagram of a system for guaranteeing operation of air traffic control safety data based on multi-model fusion;
FIG. 2 is a flow chart of a method for guaranteeing the operation of air traffic control safety data based on multi-model fusion;
FIG. 3 is a flowchart illustrating a method for securing operation of air traffic control safety data according to the present invention in step A200;
FIG. 4 is a flowchart showing a method for guaranteeing operation of air traffic control safety data based on multi-model fusion according to the present invention in step A300;
FIG. 5 is a flowchart illustrating a method for securing operation of air traffic control safety data according to the present invention in step A500;
fig. 6 is a specific flowchart of step a502 of a method for securing operation of air traffic control safety data based on multi-model fusion according to the present invention.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
It should be noted that, without conflict, the embodiments and features of the embodiments in the present application may be combined with each other, and the present application will be further described in detail with reference to the drawings and the specific embodiments.
Example 1
As shown in fig. 1, an embodiment of an air traffic control safety data operation guarantee system based on multi-model fusion according to the present invention includes an air traffic control joint department coordination module 1, an air traffic control data format unification module 2, an air traffic control safety data checking and detecting module 3, an air traffic control safety standard limiting module 4, an air traffic control requirement identification module 5, an air traffic control management update module 6, an air traffic control participant call management module 7, an air traffic control data supervision module 8, an air traffic control signal pushing module 9, and an air traffic control remote sensing information feedback module 10, wherein the air traffic control joint department coordination module 1 is used for updating air traffic control safety data in real time for monitoring an air traffic control joint department, and establishing an event processing procedure between the joint departments, and the air traffic control joint department coordination module 1 is electrically connected with the air traffic control data format unification module 2. The air traffic control department coordination module 1 allows control to be carried out through a control interface (a control interface based on Web or mobile App), an air traffic control department air traffic control security data source of air traffic control department air traffic control security data to be controlled (comprising an air traffic control department air traffic control security database type, an IP address, control, a password, an air traffic control department air traffic control security data table of the air traffic control department where the air traffic control department air traffic control security data to be controlled is located, a data name of the air traffic control department air traffic control security data to be controlled in the table and the like), or a front end supervision interface of the air traffic control department air traffic control security data to be controlled; and simultaneously, the air traffic control joint department air traffic control safety data supervision source code script (for example, an air traffic control joint department air traffic control safety data supervision script written in the python language) which is in accordance with the system design standard can be supported to be input. After the consistency setting of the control interface is completed and confirmed, the consistency setting content is written into an air traffic control department air traffic control security database (air traffic control department air traffic control security database such as mysql, postgresql, mongo and the like) by an application server (which can be realized by a micro-service mode).
The air traffic control data format unification module 2 is used for updating air traffic control department air management safety data of the air traffic control department coordination module 1 in real time, supervising the air traffic control department air management safety data to be controlled from an air traffic control department air management safety database, preprocessing the air traffic control department air management safety data to form a unified air traffic control safety data calling and archiving format, and the air traffic control data format unification module 2 is respectively and electrically connected with the air traffic control department coordination module 1, the air traffic control safety data checking and detecting module 3 and the air traffic control data supervising module 8. The air traffic control data format unification module 2 generally comprises the following sub-modules: (1) And an air traffic control department air management safety data supervision module 21 to be controlled.
The module monitors air traffic control department air traffic control safety data to be controlled or the existing normal air traffic control department air traffic control safety data of the air traffic control department air traffic control safety data to be controlled according to the air traffic control department air traffic control safety data monitoring mode set by the consistency of the module 1. This module is typically done by a computing service module. For massive air traffic control united departments of air traffic control, the air traffic control united departments of air traffic control are provided with air traffic control safety data, and the computing service module can be realized by adopting distributed computing engines such as Apache Spark, apache Spark Streaming and the like, and can also finish real-time computation by adopting an elastically extensible micro-service architecture. And the air traffic control department air traffic control safety data supervision module calls corresponding air traffic control department air traffic control safety database supervision code library to supervise the air traffic control department air traffic control safety data according to the type, IP address, control, password and the air traffic control department air traffic control safety data to be controlled of the air traffic control department air traffic control safety data. If the front end supervision interface of the air traffic control department air traffic control safety data to be controlled is configured by the control, the module acquires the air traffic control department air traffic control safety data according to the configured air traffic control department air traffic control safety data supervision address, coefficient and parameter. In order to support a richer air traffic control department air traffic control safety data supervision mode, the module also supports the control input of an air traffic control department air traffic control safety data supervision code script which accords with the specification for the calculation service module to call and acquire the air traffic control department air traffic control safety data. (2) Air traffic control department air traffic control security data invokes and archives format normalization module 22.
The module recalls and archives standard air traffic control department air traffic control safety data recognizable by the air traffic control department air traffic control safety data preprocessing module 5 and the module 8 supervised by the module 21, for example, a key (value) or an n-dimensional air traffic control department air traffic control safety data frame (DataFrame).
The empty pipe safety data checking and detecting module 3 is used for automatically finding abnormal air traffic control department empty pipe safety data in the air traffic control department air traffic control safety data table after standardization in the air traffic control data format unification module 2, and the empty pipe safety data checking and detecting module 3 is electrically connected with the air traffic control data format unification module 2 and the air traffic control department air traffic control safety data coordination empty pipe system generating and updating module 5 and the air traffic control management updating module 6 respectively. The empty pipe safety data checking and detecting module 3 can automatically find out abnormal empty pipe safety data of the air traffic control joint department in the air traffic control joint department empty pipe safety data table to be controlled after standardization in the module 2 to check and detect, and solves the defects that the efficiency of traditional artificial intelligence searching and recognizing the data needing checking and detecting is low and the coordination is easy. The module mainly comprises the following two modules: (1) The artificial intelligence air traffic control department air traffic control safety data screening submodule 31. The invention mainly solves the problem of checking and detecting air traffic control department air traffic control safety data, so that the air traffic control department air traffic control safety data needs to be screened out firstly, and in terms of implementation, the air traffic control department air traffic control safety data type is marked as managed data (2) the staff air traffic control department air traffic control safety data pair screening submodule 32, and the staff air traffic control department air traffic control safety data screening submodule is mainly implemented by the following steps:
Step 1: according to the screened artificial intelligence, automatically setting and managing air traffic control department air traffic control safety data, calculating the fluctuation range of the related service air traffic control department air traffic control safety data, and obtaining the abnormal frequency among the service air traffic control department air traffic control safety data;
step 2: and (3) manually setting and screening out the managed data by the staff according to the air traffic control security data correlation degree of different air traffic control united departments calculated in the step (1) (the correlation degree is larger than a certain threshold).
The air traffic control safety standard limiting module 4 is used for carrying out consistency setting of safety standard coefficients and parameters on air traffic control safety data in the system, and the air traffic control department air traffic control safety data rule configuration module 4 is electrically connected with the air traffic control requirement identification module 5. The air traffic control safety standard limiting module 4 is mainly used for configuring monitoring coefficients and parameters which are supported by default in the system or configuring an air traffic control united department air management safety data coordination air management system which is self-considered. When the air traffic control safety data of two air traffic control united departments are highly linearly related (consistent), convolution calculation and feature recognition extraction are carried out on the air traffic control safety data through one data, and then errors of the regressive values and the values to be controlled are calculated. Assuming that the error is subjected to normal distribution, when the error between the numerical value to be controlled and the regression value exceeds the existing error covariance by more than 2 times, secondary service popularization and application are sent out; and when the error is more than 4 times of the prior error covariance, sending out primary service popularization and application.
The air traffic control requirement identification module 5 is configured to control self-identification or default coordination of air traffic control system coefficients and parameters according to the air traffic control system configuration module 4, the air traffic control data format unification module 2 normalizes existing air traffic control department air traffic control system safety data to air traffic control system, and the air traffic control safety data check detection module 3 automatically discovers (digs) to check air traffic control department air traffic control system safety data to perform coordination air traffic control system unification, and the air traffic control requirement identification module 5 is electrically connected with the air traffic control system configuration module 3, the air traffic control data format unification module 2 and the air traffic control safety data check detection module 4 respectively; the module utilizes standardized air traffic control department air traffic control security data to be controlled after the personnel data monitored by the module 3 to uniformly identify the air traffic control system according to a system default coordinated air traffic control system model. According to the characteristics of air traffic control security data of an air traffic control united department, the following coordination rule monitoring model can be set according to consistency.
Monitoring model with dynamically updated real-time coefficients and parameters: two variables that are approximately linearly related often occur in managing air traffic control department air traffic control safety data for different industries, such as a rank score of a fund and a rank level of the fund generally conform to an approximately linear relationship. For two linear correlations (air traffic control department air traffic control security data). Firstly, carrying out convolution calculation and feature recognition extraction according to air traffic control safety data of the existing air traffic control joint department to obtain a linear model; then, calculating the errors of the linear model predicted value and the real value of the air traffic control safety data of the air traffic control joint department, and establishing a corresponding Gaussian model for the errors (namely estimating the mean value and the covariance of the errors).
The air traffic control department air traffic control safety data coordination air traffic control system self-personnel and updating module 5 can be designed to comprise the following steps:
step 1: judging whether the air traffic control combined department air traffic control safety data to be controlled is new air traffic control combined department air traffic control safety data to be controlled;
step 2: if the step 1 judges that the air traffic control joint department air traffic control safety data is new, the coordinated air traffic control system model coefficient and parameter learning is carried out, and the calculation steps are as follows:
step 2_1: assuming that the air traffic control department air traffic control safety data variables corresponding to the two to-be-coordinated inspection data are x and y;
step 2_2: and carrying out convolution calculation and feature recognition extraction according to the air traffic control security data of the existing air traffic control joint department to obtain a convolution calculation and feature recognition extraction model. The estimation method of the coefficient and the parameter can adopt a least square method, a maximum likelihood method and the like;
step 2_3: calculating errors of the convolution calculation and the feature recognition extraction model and the air traffic control safety data value of the real air traffic control joint department;
step 2_4: calculating the mean value and covariance of the existing errors to obtain coefficients and parameters of a convolutional neural network algorithm;
Step 3: if the air traffic control department air traffic control security data to be checked is not new, judging whether the current time point is the updating time point of the air traffic control department air traffic control security data to be checked. If the update time point is reached, the coefficient and the parameter of the air traffic control system model to be coordinated are updated and adjusted (the calculation method is as described above, and the air traffic control system model can be updated according to the air traffic control system security data of the existing air traffic control system and the air traffic control system in the last period of time). Otherwise, ending the current module.
The air traffic control department air traffic control security data coordination air traffic control system is introduced by a monitoring calculation method after obtaining new air traffic control department air traffic control security data to be controlled from a plurality of common automatic air traffic control system models set forth in the personnel and updating module 5:
when air traffic control department air traffic control safety data to be controlled is input, a linear model established in the prior is utilized to calculate a predicted value, then an error between the predicted value and the air traffic control department air traffic control safety data to be controlled is calculated, a Gaussian model of the error between the predicted value and a true value obtained by calculation of the error input module 4 is obtained, the probability of occurrence of the error is obtained, and when the probability is lower than a value set by consistency (the module 4 is used for configuration), service popularization and application signals of different levels are sent out.
If the module 4 establishes the mixed gaussian model in the step 2_4, the current error is substituted into the trained mixed gaussian model, and when the probability of the input numerical value is lower than the numerical value set by consistency (configured by the module 4), different levels of service promotion and application signals are sent out.
The air traffic control department air traffic control management system air traffic control safety data monitoring calculation can be realized through an Apache Spark calculation engine. The calculation can be completed through Spark Streaming or Apache Spark Flink with high real-time requirements.
The air traffic control management updating module 6 is used for supporting artificial intelligence to adjust and update the model coefficient and parameter generated by the air traffic control requirement identifying module 5, perfecting an air management system, or automatically identifying a new air management system by the artificial intelligence and sending the new air management system to a manager for confirmation, and the air traffic control management updating module 6 is electrically connected with the air traffic control safety data checking and detecting module 3 and the air traffic control requirement identifying module 5 respectively; the air traffic control combined department air traffic control safety data generated by machine learning is adopted to coordinate the air traffic control system, under the condition that the air traffic control combined department air traffic control safety data is less in the existing normal air traffic control combined department, the condition that the air traffic control system is not perfect is easy to occur, and the air traffic control requirement identification module 5 supports artificial intelligence to modify and add new rules to the automatically generated air traffic control system. The automatic generation rule is matched with the artificial intelligence rule, so that the flexibility and adaptability of the system are improved.
The air traffic control participant call management module 7 is used for completing unified coordination and management of all functional module staff in the system and distributing work tasks, and the air traffic control participant call management module 7 is electrically connected with the air traffic control system configuration module 4 and the air traffic control signal pushing module 9 respectively;
the air traffic control data supervision module 8 is configured to calculate air traffic control system comprehensive of the air traffic control department and the artificial intelligence self-identification rule of the air traffic control management update module 6, which are self-generated by the air traffic control requirement identification module 5, and determine whether the air traffic control system safety data of the newly added air traffic control department has abnormal coordination or not, where the air traffic control data supervision module 8 is electrically connected with the air traffic control requirement identification module 5, the air traffic control management update module 6 and the air traffic control signal pushing module 9;
the air traffic control signal pushing module 9 sends pushing of different service content signals to an air traffic control department air management security data manager according to the calculation result of the air traffic control data supervision module 8, and the air traffic control signal pushing module 9 is electrically connected with the air traffic control data supervision module 8 and the air traffic control remote sensing information feedback module 10 respectively. The module manages and monitors the air traffic control department air traffic control safety data output by the air traffic control data supervision module 8 to use service air traffic control safety data, wherein the service air traffic control department air traffic control safety data comprises basic air traffic control safety data triggering service popularization and application, service popularization and application level and the like, and the air traffic control department air traffic control safety data is pushed to clients through channels such as WeChat, short message, application App and the like by utilizing a message pushing system. For example: the App pushing can be realized through protocols such as MQTT, XMPP and the like, and can also be realized by calling a third party platform such as push, ali cloud mobile push, messenger carrier pigeon push and the like.
The air traffic control remote sensing information feedback module 10 is configured to receive remote sensing information feedback of air traffic control united department air traffic control safety data manager on service air traffic control safety data, and feedback the air traffic control safety data remote sensing information to the air traffic control demand identification module 5 to adjust and optimize a model according to artificial intelligent remote sensing information feedback, where the air traffic control remote sensing information feedback module 10 is electrically connected with the air traffic control demand identification module 5 and the air traffic control signal pushing module 9, respectively. The module mainly receives remote sensing information feedback empty pipe safety data which is promoted and applied by an empty pipe safety data manager of an air traffic control united department and an empty pipe safety data monitoring service of the air traffic control united department, and feeds the remote sensing information feedback empty pipe safety data remote sensing information back to an air traffic control system updating module (module 4) of the air traffic control united department to adjust and optimize the empty pipe system according to artificial intelligent remote sensing information feedback. If the artificial intelligent remote sensing information feedback sends out a signal of service promotion and application as a false signal, the remote sensing information is required to be fed back to the module 4 to timely adjust the air management system according to the current input value.
Example 2
As shown in fig. 2, an embodiment of a method for guaranteeing operation of air traffic control safety data based on multi-model fusion according to the present invention includes:
step A100, updating data in real time of air traffic control security data of a joint department of air traffic control to be controlled;
step A200, according to the real-time updating of the data, the air traffic control department air traffic control security data to be controlled is supervised from an air traffic control department air traffic control security database, and the air traffic control department air traffic control security data preprocessing convention air traffic control security data is called and archived;
step A300, searching and finding abnormal air traffic control department air traffic control safety data in the standardized air traffic control department air traffic control safety data table to conduct checking and detection;
step A400, carrying out consistency setting of safety standard coefficients and parameters on safety data of a hollow pipe in the system;
step A500, carrying out unified identification of a coordinated air traffic control system according to the air traffic control department air traffic control safety data standardized in the step A200, the air traffic control department air traffic control safety data subjected to investigation and detection in the step A300 and the super coefficient and the parameters of the automatic air traffic control system model in the step A400;
Step A600, the artificial intelligence adjusts and updates the self-generated coordination empty pipe system in the step A500 to perfect the empty pipe system;
step A700, carrying out coordination calculation on air traffic control department air traffic control security data to be controlled according to the self-generated coordination empty pipe system in the step A500 and the coordination empty pipe system modified by the artificial intelligence in the step A600, and judging whether the newly added air traffic control department air traffic control security data has coordination or abnormal data;
step A800, according to the calculation result in step A700, sending out the pushing of different service content signals to the air traffic control department air traffic control safety data manager;
and step A900, receiving remote sensing information feedback of air traffic control united department air traffic control safety data manager on using service air traffic control safety data, feeding the remote sensing information of the air traffic control safety data back to step A500, and adjusting and optimizing the unified identification rule of the check and detection.
As shown in fig. 3, the step a200 specifically includes:
step A201, updating in real time according to data, and supervising air traffic control department air traffic control safety data to be controlled or existing normal air traffic control department air traffic control safety data of air traffic control department air traffic control safety data to be controlled in an air traffic control department air traffic control safety database;
Step A202, calling and archiving the air traffic control department air traffic control safety data pre-processing appointed air traffic control safety data.
As shown in fig. 4, the step a300 specifically includes:
step A301, screening air traffic control department air traffic control security data from an air traffic control department air traffic control security data table to be controlled;
step A302, automatically setting and managing air traffic control department air traffic control safety data according to screened artificial intelligence, and calculating fluctuation range of related service air traffic control department air traffic control safety data to obtain abnormal frequency among the service air traffic control department air traffic control safety data;
and step A303, screening out the data manually set and managed by the staff according to the obtained correlation degree of the air traffic control safety data of different air traffic control united departments.
As shown in fig. 5, the step a500 specifically includes:
step A501, judging whether air traffic control department air traffic control security data to be controlled is new air traffic control department air traffic control security data to be controlled;
step A502, if the air traffic control department air traffic control system determines that the air traffic control department air traffic control system is new, carrying out coordinated air traffic control system model coefficient and parameter learning;
Step A503, if it is determined that the air traffic control unit air traffic control security data is not new air traffic control unit air traffic control security data, it is determined whether the current time point is an update time point of the air traffic control unit air traffic control security data to be checked and detected. And if the updated time point is reached, updating and adjusting the coefficient and the parameter of the empty pipe system model to be coordinated.
As shown in fig. 6, the step a502 specifically includes:
step A5021, supposing that two air traffic control united departments corresponding to the checking data to be coordinated are empty-control safety data variables;
step A5022, performing convolution calculation and feature recognition extraction according to the air traffic control safety data of the existing air traffic control joint department to obtain a convolution calculation and feature recognition extraction model;
step A5023, calculating errors of a convolution calculation and feature recognition extraction model and an air traffic control safety data value of a real air traffic control joint department;
and step A5024, calculating the mean value and covariance of the existing errors to obtain coefficients and parameters of the convolutional neural network algorithm.
The invention provides an air traffic control security data operation guarantee system and a monitoring method based on multi-model fusion, which can automatically find air traffic control integrated department air traffic control security data for checking and detecting, and greatly improve the efficiency, accuracy and coverage rate of the air traffic control integrated department air traffic control security data checking and detecting; 2. after finding out the data for checking and detecting, the air traffic control department air traffic control security data coordination air traffic control system is automatically learned according to the existing value of the air traffic control department air traffic control security data, so that the management, efficiency and coverage of the air traffic control department air traffic control security data checking and detecting are improved, and meanwhile the efficiency of air traffic control is improved.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "compliance setting", "mounting", "connecting", "fixing" and "fixing" should be interpreted broadly, and for example, they may be fixed, they may be removable, or they may be integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art in a specific case. Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various equivalent changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (10)
1. An empty pipe safety data operation guarantee system based on multi-model fusion is characterized in that the system comprises:
the air traffic control joint department coordination module (1) is used for carrying out real-time update on air traffic control safety data of the air traffic control joint departments and establishing event processing flows among the joint departments;
The air traffic control data format unification module (2) is used for updating air traffic control department air traffic control safety data in real time according to the air traffic control department air traffic control coordination module (1), supervising the air traffic control department air traffic control safety data to be controlled from an air traffic control department air traffic control safety database, preprocessing the air traffic control department air traffic control safety data to form unified air traffic control safety data calling and archiving;
the air traffic control security data check and detection module (3) is used for checking and detecting abnormal air traffic control department air traffic control security data in the air traffic control department air traffic control security data table to be controlled standardized by the air traffic control data format unification module (2);
the air traffic control safety standard limiting module (4) is used for carrying out consistency setting of safety standard coefficients and parameters on air traffic control safety data in the air traffic control safety data operation assurance system based on multi-model fusion;
the air traffic control requirement identification module (5) is used for controlling self-identification or default coordination air traffic system coefficients and parameters according to the air traffic system configuration module, carrying out unified identification on the air traffic system by the air traffic control data format unification module (2) standardized air traffic control integrated department air traffic system safety data and carrying out investigation and detection by the air traffic control integrated department air traffic system safety data by the air traffic control safety data investigation and detection module (3);
The air traffic control management updating module (6) is used for supporting artificial intelligence to adjust and update the model coefficient and the parameter which are generated by the air traffic control requirement identifying module (5) automatically, so as to perfect an air management system, or the artificial intelligence automatically identifies a new air management system and sends the new air management system to a manager for confirmation;
the air traffic control participant personnel call management module (7) is used for completing unified coordination and management of the personnel of each functional module in the system and distributing work tasks;
the air traffic control data supervision module (8) is used for integrating the artificial intelligence self-identification rule of the air traffic control management updating module (6) according to the air traffic control requirement identification module (5) self-generated air traffic control department air traffic control system, calculating the air traffic control department air control safety data to be checked newly added, and judging whether the air traffic control department air control safety data of the newly added air traffic control department has coordination or abnormal data;
the air traffic control signal pushing module (9) is used for sending pushing of different service content signals to an air traffic control department air management safety data manager according to the calculation result of the air traffic control data supervision module (8);
The air traffic control remote sensing information feedback module (10) is used for receiving remote sensing information feedback of an air traffic control joint department air traffic control safety data manager on using service air traffic control safety data, and feeding back the air traffic control safety data remote sensing information to the air traffic control requirement identification module (5) to adjust and optimize the model according to artificial intelligent remote sensing information feedback.
2. An air traffic control safety data operation assurance system based on multimodal fusion according to claim 1, characterized in that the air traffic control data format unification module (2) comprises:
the air traffic control department air traffic control safety data supervision module (21) is used for supervising the air traffic control department air traffic control safety data to be controlled or the air traffic control department air traffic control safety data of the existing normal air traffic control department according to the air traffic control department air traffic control safety data supervision mode set by the air traffic control department coordination module (1) in a consistency mode;
and the air traffic control and united department air traffic control safety data calling and archiving standardization module (22) is used for preprocessing the air traffic control and united department air traffic control safety data supervised by the air traffic control and united department air traffic control safety data supervising module (21) to be controlled, and the air traffic control and control requirement recognizing module (5) and the air traffic control and united department air traffic control safety data distinguishable by the air traffic control and data supervising module (8) are called and archived.
3. An air traffic control safety data operation assurance system based on multimodal fusion according to claim 1, characterized in that the air traffic control safety data check detection module (3) comprises:
the air traffic control security data screening submodule (31) of the artificial intelligent air traffic control joint department is used for screening out the data automatically set and managed by the artificial intelligent;
and the air management safety data screening submodule (32) is used for screening out the data manually set and managed by the staff.
4. The air traffic control safety data operation guarantee system based on multi-model fusion according to claim 1, wherein the air traffic control system model of the air traffic control system configuration module is a monitoring model with dynamically updated real-time coefficients and parameters.
5. The air traffic control safety data operation guaranteeing method based on multi-model fusion is characterized by comprising the following steps of:
a1, updating data in real time of air traffic control security data of an air traffic control joint department to be controlled;
step A2, according to the real-time updating of the data, the air traffic control department air traffic control safety data to be controlled is supervised from an air traffic control department air traffic control safety database, and the air traffic control department air traffic control safety data is called and archived;
Step A3, checking and detecting abnormal air traffic control department air traffic control security data in the standardized air traffic control department air traffic control security data table;
step A4, carrying out consistency setting of safety standard coefficients and parameters on hollow pipe safety data in the air pipe safety data operation assurance system based on multi-model fusion;
step A5, the air traffic control department air traffic control safety data according to the air traffic control department air traffic control safety data standardized in the step A2, the air traffic control department air traffic control safety data subjected to investigation and detection in the step A3 and the safety standard coefficient and the parameters in the step A4 are uniformly identified;
step A6, the artificial intelligence adjusts and updates the self-generated coordination empty pipe system in the step A5, and perfects the empty pipe system;
step A7, carrying out coordination calculation on air traffic control department air traffic control security data to be controlled according to the self-generated coordination empty pipe system in the step A5 and the coordination empty pipe system modified by the artificial intelligence in the step A6, and judging whether the newly added air traffic control department air traffic control security data has coordination or abnormal data;
and step A8, sending out the pushing of different service content signals to an air traffic control department air traffic control safety data manager according to the calculation result in the step A7.
6. The air traffic control safety data operation guarantee method based on multi-model fusion according to claim 5, wherein the step A8 further comprises a step A9: and A5, receiving remote sensing information feedback of air traffic control united department air traffic control safety data manager to service air traffic control safety data, feeding the remote sensing information of the air traffic control safety data back to the step A5, and adjusting and optimizing the check and detection unified identification rule.
7. The air traffic control safety data operation guaranteeing method based on multi-model fusion according to claim 5, wherein the step A2 specifically comprises:
step A21, updating in real time according to the data, and supervising the air traffic control department air traffic control safety data to be controlled or the existing normal air traffic control department air traffic control safety data of the air traffic control department air traffic control safety data to be controlled in an air traffic control department air traffic control safety database;
and step A22, calling and archiving the air traffic control department air traffic control safety data which are supervised.
8. The air traffic control safety data operation guaranteeing method based on multi-model fusion according to claim 5, wherein the step A3 specifically comprises:
Step A31, screening air traffic control department air traffic control security data from an air traffic control department air traffic control security data table to be controlled;
step A32, automatically setting and managing data and air traffic control department air traffic control safety data according to the screened artificial intelligence, and calculating the fluctuation range of the air traffic control department air traffic control safety data of related service air traffic control departments to obtain abnormal frequency among the air traffic control department air traffic control safety data of the service air traffic control departments;
and step A33, screening out the data manually set and managed by the staff according to the obtained correlation degree of the air traffic control safety data of different air traffic control united departments.
9. The air traffic control safety data operation guaranteeing method based on multi-model fusion according to claim 5, wherein the step A5 specifically comprises:
step A51, judging whether air traffic control department air traffic control security data to be controlled is new air traffic control department air traffic control security data to be controlled;
step A52, if the air traffic control department air traffic control system determines that the air traffic control department air traffic control system safety data is new, carrying out coordinated air traffic control system model coefficient and parameter learning;
And step A53, if the air traffic control combined department air traffic control safety data is judged not to be newly generated, judging whether the current time point is an updating time point of the air traffic control combined department air traffic control safety data to be checked and detected, and if the current time point is reached, updating and adjusting the model coefficient and the parameter of the air traffic control combined department air traffic control safety data to be coordinated.
10. The air traffic control safety data operation guarantee method based on multi-model fusion according to claim 9, wherein the step a52 specifically includes:
step A521, assume that two air traffic control joint departments air traffic control safety data variables corresponding to the checking data to be coordinated;
step A522, performing convolution calculation and feature recognition extraction according to the air traffic control security data of the existing air traffic control joint department to obtain convolution calculation and feature recognition extraction results;
step A523, calculating errors of convolution calculation and feature recognition extraction results and air traffic control safety data values of a real air traffic control joint department;
and step A524, calculating the mean and covariance of the existing errors to obtain coefficients and parameters of the convolutional neural network algorithm.
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| CN105491122A (en) * | 2015-12-02 | 2016-04-13 | 中国民用航空总局第二研究所 | System for fusing and exchanging data among multiple control centers |
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| CN115037769A (en) * | 2022-07-01 | 2022-09-09 | 成都民航空管科技发展有限公司 | SOA-based air traffic control automation system and implementation method thereof |
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| CN105491122A (en) * | 2015-12-02 | 2016-04-13 | 中国民用航空总局第二研究所 | System for fusing and exchanging data among multiple control centers |
| CN113486095A (en) * | 2020-06-15 | 2021-10-08 | 中国民用航空局空中交通管理局 | Civil aviation air traffic control cross-network safety data exchange management platform |
| CN115037769A (en) * | 2022-07-01 | 2022-09-09 | 成都民航空管科技发展有限公司 | SOA-based air traffic control automation system and implementation method thereof |
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